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www.hydrol-earth-syst-sci.net/19/3891/2015/ doi:10.5194/hess-19-3891-2015

© Author(s) 2015. CC Attribution 3.0 License.

Climate model uncertainty versus conceptual geological uncertainty

in hydrological modeling

T. O. Sonnenborg1, D. Seifert2, and J. C. Refsgaard1

1Geological Survey of Denmark and Greenland (GEUS), Department of Hydrology, Øster Voldgade 10,

1350 Copenhagen, Denmark

2ALECTIA A/S, Water & Environment, Teknikerbyen 34, 2830 Virum, Denmark

Correspondence to: T. O. Sonnenborg (tso@geus.dk)

Received: 7 April 2015 – Published in Hydrol. Earth Syst. Sci. Discuss.: 29 April 2015 Revised: 13 August 2015 – Accepted: 21 August 2015 – Published: 16 September 2015

Abstract. Projections of climate change impact are asso-ciated with a cascade of uncertainties including in CO2 emission scenarios, climate models, downscaling and impact models. The relative importance of the individual uncertainty sources is expected to depend on several factors including the quantity that is projected. In the present study the impacts of climate model uncertainty and geological model uncer-tainty on hydraulic head, stream flow, travel time and capture zones are evaluated. Six versions of a physically based and distributed hydrological model, each containing a unique in-terpretation of the geological structure of the model area, are forced by 11 climate model projections. Each projection of future climate is a result of a GCM–RCM model combina-tion (from the ENSEMBLES project) forced by the same CO2 scenario (A1B). The changes from the reference pe-riod (1991–2010) to the future pepe-riod (2081–2100) in pro-jected hydrological variables are evaluated and the effects of geological model and climate model uncertainties are quantified. The results show that uncertainty propagation is context-dependent. While the geological conceptualization is the dominating uncertainty source for projection of travel time and capture zones, the uncertainty due to the climate models is more important for groundwater hydraulic heads and stream flow.

1 Introduction

Climate change will have major impacts on human soci-eties and ecosystems (IPCC, 2007). Climate change adap-tation is, however, impeded by the large uncertainties arising

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hy-Figure 1. Model area of the Langvad Stream catchment area with

land surface elevation, streams, abstraction wells and location of the main well fields in the focus area (the clusters of wells along the streams).

drological model uncertainty and vice versa in other cases. These studies have focussed on surface water hydrological systems, while we are not aware of studies that have investi-gated the relative importance of conceptual geological model uncertainty versus climate model uncertainty.

The objective of the present study is to assess the effects of climate model uncertainty and conceptual geological un-certainty for projection of future conditions with respect to different river flow and groundwater aspects.

2 Study area and model setup 2.1 Study area

The study site has an area of 465 km2 and is located in the central part of Zealand, Denmark (Fig. 1) where focus is given to the area covered by the Langvad Stream valley sys-tem. The model area is bounded by Køge Bay in the east and by Roskilde Fjord in the north. The area is relatively hilly, with maximum elevations of approximately 100 m above sea level. Land use within the model area is dominated by agri-culture (80 %), while the remaining area is covered by for-est (10 %), urban areas (10 %) and lakes (< 1 %). The main aquifer in the area, the Limestone Formation, is overlain by Quaternary deposits of interchanging and discontinuous lay-ers of clayey moraine till and fluvial sand. Groundwater is abstracted at six main well fields in the focus area and ex-ported to Copenhagen for water supply. The study area is described in detail in Seifert et al. (2012).

2.2 Model setup

Based on the national water resource model developed by the Geological Survey of Denmark and Greenland (Henriksen et

al., 2003; Højberg et al., 2008, 2013) a hydrological model of the catchment area has been developed. The model is con-structed using the MIKE SHE/MIKE 11 modeling software (DHI, 2009a, b). MIKE SHE includes a range of alternative process descriptions and here the modules for evapotranspi-ration, overland flow, a two-layer description of the unsatu-rated zone, and the satuunsatu-rated zone including drains is used. The river model MIKE 11 links to MIKE SHE, so that water is exchanged between streams and the groundwater aquifers. Six alternative geological models using between 3 and 12 hydrostratigraphical layers (Table 1) have been established (Seifert et al., 2012). The basis of the geological models com-prises two national models (N1 and N2), two regional models (R1 and R2) and two local models (L1 and L2). All models consist from bottom to top of Paleocene limestone, Paleocene clay and Quaternary deposits. In the more complex geolog-ical models the Quaternary unit is divided into several al-ternating sand and clay layers. The location of the limestone surface and the extent of the sand aquifers differ significantly between the geological models. The six geological mod-els were incorporated into the hydrological model, resulting in six alternative hydrological models (Seifert et al., 2012). Horizontally, the models are discretized in 200×200 m cells. Vertically, the numerical layers are discretized according to the geological layers, though in model N1 and N2 the three top layers are combined into one numerical layer. Between three and ten numerical layers are used in the six models.

The models are calibrated against hydraulic head and stream discharge data from 2000–2005 and validated in the period 1995–1999 (Seifert et al., 2012) where the validation period is characterized by a groundwater abstraction that is about 20 % higher than in the calibration period. Generally, the simulation results for the validation period are slightly inferior to the results for the calibration period, but the statis-tical values have the same magnitude for the two periods. The calibration results (Table 1) reveal quite large differences in the match to hydraulic head (represented by the mean error, ME, and the root mean square value, RMS), the stream dis-charge (given by the Nash–Sutcliffe coefficient,E, and the relative water balance error,Fbal)using the different geolog-ical models. Some models are more suitable for stream flow simulations (e.g., N2) while other models are stronger on hy-draulic heads (e.g., L2). However, based on an integrated evaluation the calibration/validation statistics no model is generally superior to the others. More details on the model setup including historical climate data and model calibration and validation can be found in Seifert et al. (2012).

[image:2.612.50.286.66.230.2]
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Table 1. Geological models of the Langvad Stream catchment area. Calibration statistics are indicated by the mean error (METS) and root mean square error of hydraulic head time series (RMSTS), the Nash–Sutcliffe coefficient (E) and the water balance (Fbal)for stream

discharge.

Name R1 R2 L1 L2 N1 N2

No. of hydro- 3 5 7 7 11 12

stratigraph. layers

No. of numerical 3 5 7 7 9 10

layers in model

Reference (Roskilde (Roskilde (Københavns (Københavns (Henriksen (Højberg Amt, 2002) Amt, 2003) Energi, 2005) Energi, 2005) et al., 1998) et al., 2008) METS(m) −1.41 −0.20 0.31 −0.16 1.38 −0.19

RMSTS(m) 6.52 3.12 2.08 2.01 4.41 4.82 E(−) 0.58 0.58 0.17 −0.12 0.63 0.75

[image:3.612.88.512.107.241.2]

Fbal(%) −17 −8 −2 −2 −2 −2

Table 2. Matrix of ENSEMBLES climate models with GCM–RCM

pairings used for the climate models (GCM denotes global cli-mate model, RCM denotes regional clicli-mate model). From Seaby et al. (2013).

GCM HadCM3 ECHAM5 ARPEGE BCM2 RCM

HadRM3 X

REMO X

RM5.1 X

HIRHAM5 X X X

CLM X

RACMO2 X

RegCM3 X

RCA3 X X

2.3 Climate data

Climate projections representing the period 2081–2100 are obtained from Seaby et al. (2013) using results from 11 cli-mate models from the ENSEMBLES matrix (Christensen et al., 2009) of global and regional climate model pairings (GCM–RCM), Table 2. Seaby et al. (2013) analyzed the im-pact of the length of the reference and the future periods and found that period lengths over 15 years appeared suitable for precipitation. Hence, comparing two 20-year periods is as-sumed to be adequate for the particular study area.

The delta change (DC) method (Hay et al., 2000; van Roosmalen et al., 2007) is used as a downscaling approach on precipitation (P), reference evapotranspiration (ETref) and temperature (T). The delta change factors for Zealand are derived by comparing monthly mean values of past and fu-ture climate data from the climate models (Seaby, 2013). The model projections of future climate changes vary signifi-cantly with both drier and wetter future climate, indicated by delta change factors on precipitation, ranging between 0.83 and 1.17 on an annual basis. However, major differences

be-tween the models are also found with respect to the sea-sonal signal. To obtain time series of future climate, observed records of P and ETref in the control period (1991–2010) are multiplied by the monthly delta change factors (1P and 1ETref), while the temperature delta change values (1T) are added to the observed time series ofT. The reliability of the DC method for projecting changes has rightfully been questioned by Teutschbein and Seibert (2013) who found that more advanced methods were more reliable. In our spe-cific case Seaby et al. (2013) compared the DC method with a double gamma distribution based scaling (DBS), show-ing that both methods were equally good in capturshow-ing the mean monthly as well as the seasonal climate characteristics in temperature, precipitation and potential evapotranspira-tion when tested against observed data for 1991–2010. Seaby (2013) further showed that when propagating climate projec-tions for 2071–2100 through the same hydrological model type as used in our study, the results for the discharge and groundwater head characteristics used in our study are almost identical for the two bias correction methods. This confirms the results of van Roosmalen et al. (2011) and justifies the use of the simple DC method for our particular application.

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3 Methodology

Results from the six hydrological models forced by climate projections from the 11 climate models (total of 66 model simulations) are extracted and the variance caused by the ge-ological model and the climate model is derived. The results are also compared to results representing the reference pe-riod 1991–2010 that covers both the calibration and valida-tion periods, to quantify the changes in hydraulic head (1h) in the limestone aquifer in the focus area (Fig. 1), changes in stream discharge (1Q) at a downstream gauging station in the Langvad Stream system, travel time (1T) and capture zone area (1Acap)for the well fields in the focus area. The change in hydrological variables is caused by climate change only as the geology is the same for both reference and sce-nario climate.

3.1 Hydraulic head and stream discharge

The mean hydraulic head (h) in the limestone aquifer within the focus area is extracted from all model simulations and the change in hydraulic head (1h) as a result of changing climate is calculated.

A large part of the precipitation is expected to flow di-rectly to the streams, either as surface runoff or through the drains, especially during the winter season. Hence, the total stream discharge is expected to be highly sensitive to changes in climate. In order to capture the effect of climate change on the groundwater-dominated base flow, stream discharge re-sults from the summer period (June, July and August) are extracted at the downstream discharge station, st. 52.30 (see Fig. 1).

3.2 Travel time

Travel times from the water table to the well fields are esti-mated by forward particle tracking using MIKE SHE. Parti-cles are initially located randomly in the upper 1–3 numerical layers depending on how the geology is represented by the numerical layers in the models. The sum of particles in the vertical direction is 200 particles per cell, resulting in about 2 million particles per model. The flow solution on which the particle tracking simulation is based is obtained by recycling the flow results for the simulation period (1991–2010 for the reference period and 2081–2100 for the future climate pe-riod). After 1000 years of simulation the end points are reg-istered and particles with end points at the well fields are extracted. Since the thickness of the numerical layers vary considerably between the models, only particles originating from the upper 10 m of the saturated zone are used for the travel time assessment in order to get comparable results. The median travel time (T) at each well field is calculated for each of the 11 future climate projections and for the ref-erence climate. The changes in travel time (1T) from the ref-erence climate to the future climate projections are also

cal-Figure 2. Methodology for estimation of and change in capture

zone area for a well field.

culated. Precipitation may affect the hydraulic heads and the hydraulic gradients in a specific area which affects ground-water discharge and hence the flow velocity. Additionally, flow paths to the abstraction well may change as the size of the recharge area changes, see below. Climate change is therefore expected to impact travel time to the abstraction wells.

3.3 Capture zone

[image:4.612.309.546.64.261.2]
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[image:5.612.309.547.64.161.2]

Figure 3. Based on results where each of the six hydrological

mod-els are forced by 11 climate model projections: (a) box plot of the simulated mean hydraulic head,h, in the limestone aquifer in the focus area and (b) box plot of the change inh from reference to future scenarios.

4 Results

4.1 Uncertainty on hydraulic head

The matrix of results on mean hydraulic head within the fo-cus area as a function of climate scenario and geological model is presented in Table 3. In the two columns to the right and the two bottom rows, the mean and standard deviation of the results are listed. Changes in hydraulic head between the reference climate simulation and the scenario climate simu-lation are indicated in brackets.

[image:5.612.50.286.67.198.2]

No reference geology is defined and as due to the DC method, the same reference climate is used for all projec-tions, the uncertainty on the change in hydraulic head caused by climate (SD climate, bottom row in Table 3) therefore equals the uncertainty on the absolute heads. In Table 4, the uncertainty on the absolute head values is summarized to-gether with results on the change in heads due to climate change. In Fig. 3 the results are illustrated using box plots both with respect to absolute values (Fig. 3a) and with respect to changes from the reference to the future period (Fig. 3b). Hence, Fig. 3a corresponds to the left two columns in Ta-ble 4 while Fig. 3b illustrates the results summarized in the three columns to the right in Table 4. With respect to the ab-solute hydraulic head values, Fig. 3a and Table 4, the impact of geological model and climate model is comparable. The difference between the mean hydraulic head using the six geologies is primarily caused by differences in calibration results given by the mean errors (ME) (see Table 1) since cli-mate change affects the mean hydraulic head of the individ-ual geological model comparatively. For model R2, changes in mean head between −1.12 and 0.82 m are found with a standard deviation of 0.66 m. The mean standard deviation on all six models is 0.52 m (Tables 3 and 4) which is in the same order of magnitude as the standard deviation caused by the different geological models amounting to 1.03 m.

Figure 4. Simulated change in mean hydraulic head of the

lime-stone aquifer in the focus area using six geological models and 11 climate models.

When the changes in hydraulic head are compared across geological models (Fig. 3b and Table 4), it is clear that the effect of geology is relatively small. Some of the geolog-ical models are more sensitive to the changes in climate (e.g., R2) than others (e.g., L2), represented by the length of the whiskers for each geological model in Fig. 3. Changes in hydraulic head that are up to twice as high are found for the most sensitive models compared to the models that are relatively insensitive. However, larger differences in hy-draulic head change are found across climate models repre-sented by the difference between the upper and lower end of the whiskers. A two-factor analysis of variance shows that the climate model has more impact on the change in hy-draulic head than the geological model, asFclimate=104.6 (Fcrit=2.0) andFgeology=1.2 (<Fcrit=2.4). The same conclusion can also be drawn from Table 4 by comparing the standard deviation on the changes in hydraulic head (h) caused by geological models (0.11 m) with the standard de-viation caused by climate models (0.52 m).

Figure 4 also shows that the direction and the magnitude of the change in hydraulic head depend primarily on the cli-mate model. Three of the clicli-mate models result in decreas-ing hydraulic heads, with values rangdecreas-ing between−0.28 and −1.16 m depending on the geological model and the climate model. The remaining eight climate models all result in in-creasing hydraulic heads in the limestone aquifer between 0.08 and 0.82 m.

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[image:6.612.90.509.117.461.2]

Table 3. Simulated mean hydraulic head in the limestone aquifer in the focus area for the reference scenario and the scenario climates.

Changes in mean hydraulic head from reference to scenario climate are listed in brackets. “Mean geology” and “SD geology” are the average and the standard deviation of the results from the hydrological models for each climate scenario. “Mean climate” and “SD climate” the average and the standard deviation of the results from the different climate models used in each hydrological model.

R1 R2 L1 L2 N1 N2 Mean

geology SD geology

hmean, m

Reference climate 21.0 20.7 20.0 20.4 18.5 19.1 19.9 1.0 ARPEGE-RM5.1 20.0

(−1.01) 19.6 (−1.12)

19.3 (−0.73)

19.8 (−0.61)

17.3 (−1.16)

18.1 (−0.94)

19.0 (−0.93)

1.07 (0.22) ARPEGE-HIRHAM5 20.0

(−1.00) 19.6 (−1.07)

19.3 (−0.69)

19.8 (−0.59)

17.4 (−1.08)

18.2 (−0.88)

19.1 (−0.89)

1.04 (0.21) BCM-HIRHAM5 21.2 (0.17) 20.9 (0.19) 20.1 (0.11) 20.5 (0.08) 18.6 (0.14) 19.2 (0.12) 20.1 (0.13) 1.00 (0.04) BCM-RCA3 21.5 (0.47) 21.2 (0.52) 20.3 (0.31) 20.7 (0.25) 18.8 (0.40) 19.4 (0.33) 20.3 (0.38) 1.02 (0.10) ECHAM-HIRHAM5 21.7 (0.71) 21.5 (0.82) 20.5 (0.48) 20.8 (0.39) 19.0 (0.60) 19.6 (0.49) 20.5 (0.58) 1.05 (0.16) ECHAM-RegCM3 21.2 (0.23) 20.9 (0.23) 20.1 (0.13) 20.5 (0.09) 18.6 (0.14) 19.2 (0.11) 20.1 (0.15) 1.02 (0.06) ECHAM-RACMO2 21.5 (0.45) 21.2 (0.48) 20.3 (0.28) 20.6 (0.21) 18.8 (0.34) 19.4 (0.27) 20.3 (0.34) 1.04 (0.11) ECHAM-REMO 21.2 (0.21) 20.9 (0.21) 20.1 (0.13) 20.5 (0.10) 18.6 (0.15) 19.2 (0.12) 20.1 (0.15) 1.01 (0.05) ECHAM-RCA3 21.5 (0.50) 21.2 (0.55) 20.3 (0.33) 20.7 (0.25) 18.8 (0.40) 19.4 (0.32) 20.3 (0.39) 1.03 (0.11) HADQ0-CLM 21.4 (0.39) 21.1 (0.40) 20.2 (0.24) 20.6 (0.18) 18.7 (0.28) 19.3 (0.23) 20.2 (0.29) 1.03 (0.09) HADQ0-HadRM3 20.6

(−0.39) 20.2 (−0.47)

19.7 (−0.32)

20.1 (−0.28)

18.0 (−0.47)

18.7 (−0.41)

19.6 (−0.39)

1.02 (0.08) Mean climate 21.1

(0.07) 20.7 (0.07) 20.0 (0.02) 20.4 (0.01) 18.4 (−0.02)

19.1 (−0.02)

20.0 (0.02a)

1.03 (0.11b) SD climate 0.60

(0.60) 0.66 (0.66) 0.42 (0.42) 0.34 (0.34) 0.60 (0.60) 0.49 (0.49) 0.52 (0.52c)

1.07 (0.51a)

aMean and standard deviation based on all the numbers in the matrix.bMean of the standard deviations of geological models.cMean of the standard

deviations of climate models.

The same tendency is found for the other climate models. Hence, since the mean change in hydraulic head is expected to depend on the changes in precipitation and evapotranspira-tion, the mean standard deviation on heads from the different geological models are compared to the change in the net pre-cipitation (here represented by prepre-cipitation minus reference evapotranspiration, P−ETref). The result (Fig. 5) reveals a clear linear tendency for increasing uncertainty caused by the geological model as the changes projected by the climate model differ from the present climate, where the model was calibrated. Hence, as the future climate moves away from the baseline, the more sensitive the results are with respect to the conceptual geological model and the higher the projection uncertainty might be expected to be.

4.2 Uncertainty on stream discharge

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[image:7.612.120.476.117.369.2]

Table 4. Results of variance analysis with respect to climate models and geological models on (1) absolute mean values and (2) changes in

mean values compared to results obtained using reference climate with respect to hydraulic head, discharge (annual and summer distance), travel time and catchment area. All variance components (columns denoted “Geology” and “Climate”) are presented as standard deviations. The column “Mean change” denotes the projected mean change.

Absolute values Change relative to reference climate Location Geology Climate Mean change Geology Climate Head, m Focus area 1.03 0.52 0.02 0.11 0.52 Annual distance, st. 52.30 0.08 0.32 0.08 0.01 0.32 m3s−1

Summer distance, st. 52.30 0.21 0.14 0.00 0.05 0.14 m3s−1

Travel time, Assermølle 30.7 6.4 −0.2 4.6 6.4 years Gevninge 60.3 4.1 0.6 2.5 4.1 Hule Mølle 36.4 4.9 1.6 7.1 4.9 Kornerup 81.0 2.8 0.6 2.7 2.8 Lavringe 58.5 2.4 1.5 2.4 2.4 Ramsø 66.5 4.2 0.8 3.4 4.2 Catchment area, Assermølle 13.0 1.6 2.4 1.4 1.6 km2 Gevninge 1.6 0.6 0.8 0.5 0.6 Hule Mølle 6.2 0.5 0.7 0.5 0.5 Kornerup 15.9 1.0 2.9 1.6 1.0 Lavringe 6.7 0.3 0.9 0.4 0.3 Ramsø 10.5 0.4 1.0 0.5 0.4

Figure 5. Standard deviations of the change in hydraulic head from

the geological models (Table 3), compared with the change in the reference net precipitation (P−ETref).

In Fig. 6b the box plot of the change in summer discharge from the reference period to the future scenarios shows that the response in summer stream discharge from the differ-ent geological models is similar when the median value is considered. On average, the mean change in summer dis-charge is zero; see Table 4. The difference between upper and lower whiskers indicates that the impact of climate mod-els on the projection of the change in summer stream dis-charge is significant, with changes from−0.3 to 0.3 m3s−1. The standard deviations listed in Table 4 show that the un-certainty on the change in summer discharge caused by ge-ology is 0.05 m3s−1, whereas the uncertainty caused by the

climate model amounts to 0.14 m3s−1, i.e., the climate un-certainty is largest although the contributions are in the same order of magnitude. With respect to annual mean discharge (Qa)(see Table 4) climate uncertainty is much higher than geological uncertainty, especially when the change in dis-charge is considered. This shows that the uncertainty on an-nual mean stream discharge is much more sensitive to cli-mate change than to the geological model. Summer dis-charge, where groundwater–river interactions are relatively more important, is much more affected by the uncertainty in geology.

4.3 Uncertainty on travel time

The travel time of the groundwater abstracted at each of the six well fields in the focus area has been quantified and listed in Table 4. The results obtained at the six wells fields are sim-ilar, and therefore only results on travel times and changes in travel time are illustrated for one of the well fields, Lavringe; see Fig. 7.

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Figure 6. (a) Box plot of the simulated mean summer stream

dis-charge (Qs)in a downstream discharge station (st. 52.30) using in-put from 11 climate models, and (b) box plot of the change inQs

from reference to future scenarios.

Figure 7. (a) Box plot of the simulated median travel time to

Lavringe well field, and (b) box plot of the percentage change in median travel time from reference to future scenarios.

Figs. 3a and 6a, respectively, it is clear that the effect of the geological model is crucial when travel times are considered. The standard deviations on geological models, in the order of 30–80 years (Table 4), are significantly higher than the stan-dard deviations on climate models, in the range of 2–6 years. Hence, the climate model has limited impact on the absolute travel time predictions. This indicates that climate changes do not notably change the flow pattern that controls the flow paths and hereby the travel time of the groundwater from the surface to, e.g., an abstraction well.

If changes in travel time from the reference to future cli-mate (Fig. 7b and Table 4) are considered, it is seen that the role of the geological model on the change in travel time is similar to the impact of climate change. The mean standard deviation on the change caused by climate models and ge-ological models are of the same magnitude with values of approximately 2 years for Lavringe well field. At the other well fields comparative results are also obtained with values in the range of 2.5 to 7.1 years (Table 4). This is in contrast to the results for hydraulic head and stream discharge where the climate signal was the most important factor for the changes.

Figure 8. (a) Box plot of the simulated capture zone area for

Lavringe well fields and (b) box plot of the percentage change in capture zone area from reference to future scenarios.

4.4 Uncertainty on capture zones

Figure 8a shows results on capture zone area from Lavringe well field. Capture zone areas between 20 and 40 km2 are found for the different geological models. If all six well fields in the focus area (Fig. 1) are considered, the capture zone area varies with a factor of 2–3 using different geological models. In comparison, the effect of climate model on the uncertainty is relatively small. For most models the change in capture zone area caused by climate change (Fig. 8b) amounts to less than 2 km2, corresponding to less than 10 % of the reference area. Hence, the results with regard to the capture zone area are very similar to those found for travel time (Fig. 7).

The impacts of climate model and conceptual geology on the capture zone locations are illustrated for Gevninge and Lavringe well fields in Fig. 9. On the left side, the uncertainty of the capture zones using different geological models is il-lustrated. To the right, the impact of using different climate models is shown. It is clear that relatively large differences between capture zone areas are found when multiple geolog-ical models are used, whereas almost identgeolog-ical capture zones are predicted for the 11 climate models.

5 Discussion

In Table 4 the uncertainties caused by the climate model and geological model are summarized, both with respect to the absolute level in the future situation and the change from the reference to the future situation. The results on the abso-lute values reflect the differences in model calibration, which in turn affects the results in the future climate. It should be noted that no calibration has been carried out with respect to travel time and catchment area.

[image:8.612.51.287.67.199.2] [image:8.612.51.284.267.397.2]
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[image:9.612.50.286.66.194.2]

Figure 9. Uncertainty of catchment areas for two well fields

us-ing (a) six geological models with the same climate model, and (b) 11 climate models with the same geological model.

hydraulic heads from reference to future climate are consid-ered, the climate model is more important for the uncertainty than the geology (difference of a factor of 5). Hence, in this case the choice of climate model is very important for the hydrological projection and the uncertainty on the changes in future hydraulic head levels.

The results for summer stream discharge (Qs)are some-what similar. The uncertainty on the absolute discharge is almost equally controlled by the geological model and the climate model, which is comparable to the results for hy-draulic head. If the change in summer discharge is consid-ered, the uncertainty caused by the climate model is a fac-tor of 3 higher than geological uncertainty. Hence, climate model uncertainty is most important but both sources of un-certainty are significant. With respect to annual mean dis-charge (Qa), the impact from climate model uncertainties in the absolute discharge is a factor of 4 higher than the geolog-ical uncertainty. If the change from reference to future period is considered, the results are even more clear. Almost all the uncertainty is caused by the climate model, whereas the ge-ology has almost no impact on the results (standard devia-tions of 0.01 m3s−1vs. 0.32 m3s−1). Therefore, the climate model projection is extremely important for results on future annual mean stream discharge. The relatively small impact of the geological model is probably explained by the clayey top soils in the catchment that cause discharge to be dominated by shallow flow components such as overland flow and drain flow, especially in the wet season (winter).

The uncertainty on absolute travel time (left two columns in Table 4) is dominated by the geological model with stan-dard deviations of up to about 80 years, whereas the uncer-tainties due to climate model only amount to a few years. Hence, in this case the geological model uncertainty is by far the most important source of uncertainty and the impact of climate model uncertainty can almost be ignored. How-ever, the uncertainties in the changes (the column to the right) caused by geology is in the same order of magnitude as the impact from climate model. The same type of results is

ob-tained as for capture zones (Fig. 8). The geological model dominates the uncertainty on the absolute capture zone area while the uncertainties in geology and climate have a com-parable, and relatively small, effect on the change in capture zone location.

It should be noted that travel time and capture zone loca-tion were not included in the model calibraloca-tion where only observations on hydraulic head and stream discharge were matched by the models. Hence, travel time and capture area were not constrained against a common target and larger dif-ferences between the results from the six models can there-fore be expected. Additionally, only model parameters (e.g., hydraulic conductivity) but not the geological structure were adjusted to fit the observations, and possible structural er-rors in the geological models are therefore, at least partially, compensated for by the estimated model parameters. Hence, larger differences are expected between model predictions of travel time and capture zone, especially since the geological structure has been shown to be crucial for variables as travel time and capture zone that depend on flow path (Seifert et al., 2008; He et al., 2013).

It was also found that when the models are used for sim-ulating conditions beyond the calibration base, i.e., used to simulate situations or type of data, which they have not been calibrated against, the differences in the geological models become more important and significant differences in the model results should be expected. Hence, the uncertainty caused by the conceptual geological model increases as the climate moves away from the baseline conditions.

Our findings are based on results from a specific case study with specific geological conditions and hence the general ap-plicability of our conclusions for other locations needs to be considered with caution. As we are not aware of other studies that have reported results from comparison of climate model uncertainty and conceptual geological model uncertainty we are not able to provide firm generic conclusions on this spe-cific aspect. However, our findings confirm the conclusions of previous studies that conceptual geological uncertainty is an important source of uncertainty in groundwater model-ing (Neumann, 2003; Bredehoeft, 2005) and that it becomes more and more dominating compared to other sources the further away model predictions are from the calibration base (Refsgaard et al., 2012).

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im-pacts on high flows were dominated by climate model uncer-tainty, while hydrological model structure uncertainty con-tributed significantly to low flows. Hence, our results on the travel times and capture zones are examples of where climate change uncertainty does not matter in practice (Refsgaard et al., 2013).

6 Conclusions

Based on hydrological model simulation using a combina-tion of six geological models and projeccombina-tions from 11 cli-mate models, the following conclusions are derived. (1) Cli-mate model uncertainty is important for projection of hy-draulic head and stream discharge. Especially for stream dis-charge, the uncertainty is dominated by the climate model. (2) Geological model uncertainty is important for projec-tion of hydraulic head and the uncertainty becomes larger as the climate signal moves away from the baseline condi-tions. (3) Geological model uncertainty has a relatively small effect on the projections of stream discharge, even though summer stream discharge is analyzed where groundwater– river interactions control a relatively high fraction of the to-tal discharge. (4) The uncertainty on travel times and cap-ture zones to well fields is dominated by geological model uncertainty. This uncertainty is controlled by the geological structure which is not constrained during the calibration pro-cess. The impact and hence the choice of climate model is relatively insignificant.

Acknowledgements. This work was funded by a grant from the

Danish Strategic Research Council for the project Hydrological Modelling for Assessing Climate Change Impacts at different Scales (HYACINTS – www.hyacints.dk) under contract DSF-EnMi 2104-07-0008. Lauren Seaby, Geological Survey of Denmark and Greenland, is acknowledged for providing the delta change factors used for downscaling the results from the climate models. Edited by: J. Seibert

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Figure

Figure 1. Model area of the Langvad Stream catchment area withland surface elevation, streams, abstraction wells and location ofthe main well fields in the focus area (the clusters of wells along thestreams).
Table 2. Matrix of ENSEMBLES climate models with GCM–RCMpairings used for the climate models (GCM denotes global cli-mate model, RCM denotes regional climate model)
Figure 2. Methodology for estimation of and change in capturezone area for a well field.
Figure 4. Simulated change in mean hydraulic head of the lime-stone aquifer in the focus area using six geological models and 11climate models.
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References

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